5 citations · 11 across the 13 of their papers we have counts for
18 papers
Causally Evaluating the Learnability of Formal Language Tasks
Vésteinn Snæbjarnarson, Anej Svete, Josef Valvoda +3
Language models, as multi-task learners, acquire a wide range of abilities during training. A fundamental question is how much task-specific data is needed to learn a given task. A…
Training Neural Networks as Recognizers of Formal Languages
Alexandra Butoi, Ghazal Khalighinejad, Anej Svete +3
Characterizing the computational power of neural network architectures in terms of formal language theory remains a crucial line of research, as it describes lower and upper bounds…
HR-Agent: A Task-Oriented Dialogue (TOD) LLM Agent Tailored for HR Applications
Weijie Xu, Jay Desai, Fanyou Wu +2
Recent LLM (Large Language Models) advancements benefit many fields such as education and finance, but HR has hundreds of repetitive processes, such as access requests, medical cla…
A Probability--Quality Trade-off in Aligned Language Models and its Relation to Sampling Adaptors
Naaman Tan, Josef Valvoda, Tianyu Liu +4
The relationship between the quality of a string, as judged by a human reader, and its probability, under a language model undergirds the development of better…
What Languages are Easy to Language-Model? A Perspective from Learning Probabilistic Regular Languages
Nadav Borenstein, Anej Svete, Robin Chan +5
What can large language models learn? By definition, language models (LM) are distributions over strings. Therefore, an intuitive way of addressing the above question is to formali…
Towards Explainability in Legal Outcome Prediction Models
Josef Valvoda, Ryan Cotterell
Current legal outcome prediction models - a staple of legal NLP - do not explain their reasoning. However, to employ these models in the real world, human legal actors need to be a…
The Ethics of Automating Legal Actors
Josef Valvoda, Alec Thompson, Ryan Cotterell +1
The introduction of large public legal datasets has brought about a renaissance in legal NLP. Many of these datasets are comprised of legal judgements - the product of judges decid…
The Architectural Bottleneck Principle
Tiago Pimentel, Josef Valvoda, Niklas Stoehr +1
In this paper, we seek to measure how much information a component in a neural network could extract from the representations fed into it. Our work stands in contrast to prior prob…
An Ordinal Latent Variable Model of Conflict Intensity
Niklas Stoehr, Lucas Torroba Hennigen, Josef Valvoda +3
Measuring the intensity of events is crucial for monitoring and tracking armed conflict. Advances in automated event extraction have yielded massive data sets of "who did what to w…
Prompting for a conversation: How to control a dialog model?
Josef Valvoda, Yimai Fang, David Vandyke
Dialog modelling faces a difficult trade-off. Models are trained on a large amount of text, yet their responses need to be limited to a desired scope and style of a dialog agent. B…
On the Role of Negative Precedent in Legal Outcome Prediction
Josef Valvoda, Ryan Cotterell, Simone Teufel
Every legal case sets a precedent by developing the law in one of the following two ways. It either expands its scope, in which case it sets positive precedent, or it narrows it, i…
Benchmarking Compositionality with Formal Languages
Josef Valvoda, Naomi Saphra, Jonathan Rawski +2
Recombining known primitive concepts into larger novel combinations is a quintessentially human cognitive capability. Whether large neural models in NLP can acquire this ability wh…
A Word on Machine Ethics: A Response to Jiang et al. (2021)
Zeerak Talat, Hagen Blix, Josef Valvoda +3
Ethics is one of the longest standing intellectual endeavors of humanity. In recent years, the fields of AI and NLP have attempted to wrangle with how learning systems that interac…
What About the Precedent: An Information-Theoretic Analysis of Common Law
Josef Valvoda, Tiago Pimentel, Niklas Stoehr +2
In common law, the outcome of a new case is determined mostly by precedent cases, rather than by existing statutes. However, how exactly does the precedent influence the outcome of…
Analyzing Neural Discourse Coherence Models
Youmna Farag, Josef Valvoda, Helen Yannakoudakis +1
In this work, we systematically investigate how well current models of coherence can capture aspects of text implicated in discourse organisation. We devise two datasets of various…
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection
Ekaterina Vylomova, Jennifer White, Elizabeth Salesky +25
A broad goal in natural language processing (NLP) is to develop a system that has the capacity to process any natural language. Most systems, however, are developed using data from…
Information-Theoretic Probing for Linguistic Structure
Tiago Pimentel, Josef Valvoda, Rowan Hall Maudslay +3
The success of neural networks on a diverse set of NLP tasks has led researchers to question how much these networks actually ``know'' about natural language. Probes are a natural…
A Tale of a Probe and a Parser
Rowan Hall Maudslay, Josef Valvoda, Tiago Pimentel +2
Measuring what linguistic information is encoded in neural models of language has become popular in NLP. Researchers approach this enterprise by training "probes" - supervised mode…